28 citations · 29 across the 2 of their papers we have counts for
6 papers
Federated Pruning: Improving Neural Network Efficiency with Federated Learning
Rongmei Lin, Yonghui Xiao, Tien-Ju Yang +4
Automatic Speech Recognition models require large amount of speech data for training, and the collection of such data often leads to privacy concerns. Federated learning has been w…
PAM: Understanding Product Images in Cross Product Category Attribute Extraction
Rongmei Lin, Xiang He, Jie Feng +4
Understanding product attributes plays an important role in improving online shopping experience for customers and serves as an integral part for constructing a product knowledge g…
Regularizing Neural Networks via Minimizing Hyperspherical Energy
Rongmei Lin, Weiyang Liu, Zhen Liu +5
Inspired by the Thomson problem in physics where the distribution of multiple propelling electrons on a unit sphere can be modeled via minimizing some potential energy, hyperspheri…
Deformable Part Networks
Ziming Zhang, Rongmei Lin, Alan Sullivan
In this paper we propose novel Deformable Part Networks (DPNs) to learn {\em pose-invariant} representations for 2D object recognition. In contrast to the state-of-the-art pose-awa…
Learning towards Minimum Hyperspherical Energy
Weiyang Liu, Rongmei Lin, Zhen Liu +4
Neural networks are a powerful class of nonlinear functions that can be trained end-to-end on various applications. While the over-parametrization nature in many neural networks re…
Decoupled Networks
Weiyang Liu, Zhen Liu, Zhiding Yu +5
Inner product-based convolution has been a central component of convolutional neural networks (CNNs) and the key to learning visual representations. Inspired by the observation tha…